The spine journal : official journal of the North American Spine Society · 2025
Normalizing spinal cord compression measures in degenerative cervical myelopathy.
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Bédard Sandrine, Valošek Jan, Seif Maryam, Curt Armin, Schading-Sassenhausen Simon, Pfender Nikolai, Freund Patrick, Hupp Markus, Cohen-Adad Julien
Аннотация
BACKGROUND CONTEXT: Accurate and automatic MRI measurements are relevant for assessing spinal cord compression severity in degenerative cervical myelopathy (DCM) and guiding treatment. The widely-used maximum spinal cord compression (MSCC) index has limitations. Firstly, it normalizes the anteroposterior cord diameter by that above and below the compression but does not account for cord size variation along the superior-inferior axis, making MSCC sensitive to compression level. Secondly, cord shape varies across individuals, making MSCC sensitive to this variability. Thirdly, MSCC is typically calculated by an expert-rater from a single sagittal slice, which is time-consuming and prone to variability.
PURPOSE: This study proposes a fully automatic pipeline to compute MSCC.
DESIGN: We developed a normalization strategy for traditional MSCC (anteroposterior diameter) using a healthy adults database (n = 203) to address cord anatomy variability across individuals and evaluated additional morphometrics (transverse diameter, area, eccentricity, and solidity).
PATIENT SAMPLE: DCM patient cohort of n = 120.
OUTCOME MEASURES: Receiver operating characteristic (ROC) and area under the curve (AUC) were used as evaluation metrics.
METHODS: We validated the method in a mild DCM patient cohort against manually derived morphometrics and predicted the therapeutic decision (operative/conservative) using a stepwise binary logistic regression incorporating demographics and clinical scores.
RESULTS: The automatic and normalized MSCC measures correlated significantly with clinical scores and predicted the therapeutic decision more accurately than manual MSCC. Significant predictors included upper extremity sensory dysfunction, T2w hyperintensity, and the proposed MRI-based measures. The model achieved an area under the curve of 0.80 in receiver operating characteristic analysis.
CONCLUSION: This study introduced an automatic method for computing normalized measures of cord compressions from MRIs, potentially improving therapeutic decisions in DCM patients. The method is open-source and available in Spinal Cord Toolbox v6.0 and above.
PURPOSE: This study proposes a fully automatic pipeline to compute MSCC.
DESIGN: We developed a normalization strategy for traditional MSCC (anteroposterior diameter) using a healthy adults database (n = 203) to address cord anatomy variability across individuals and evaluated additional morphometrics (transverse diameter, area, eccentricity, and solidity).
PATIENT SAMPLE: DCM patient cohort of n = 120.
OUTCOME MEASURES: Receiver operating characteristic (ROC) and area under the curve (AUC) were used as evaluation metrics.
METHODS: We validated the method in a mild DCM patient cohort against manually derived morphometrics and predicted the therapeutic decision (operative/conservative) using a stepwise binary logistic regression incorporating demographics and clinical scores.
RESULTS: The automatic and normalized MSCC measures correlated significantly with clinical scores and predicted the therapeutic decision more accurately than manual MSCC. Significant predictors included upper extremity sensory dysfunction, T2w hyperintensity, and the proposed MRI-based measures. The model achieved an area under the curve of 0.80 in receiver operating characteristic analysis.
CONCLUSION: This study introduced an automatic method for computing normalized measures of cord compressions from MRIs, potentially improving therapeutic decisions in DCM patients. The method is open-source and available in Spinal Cord Toolbox v6.0 and above.